Jul 2026· International Seminar on Intelligent Technology and Its Applications· pp. 529-534· 0 citations· 31 references
Abstract
This study proposes speech command classification using MFCC features and SVM with GridSearchCV hyperparameter optimization. Evaluating RBF/linear kernels, C (0.1-100), and gamma (0.001-scale) on Google Speech Commands Dataset (8 classes), the optimal configuration (RBF, $\mathbf{C}=\mathbf{1 0}$, gamma=0.01) as the best configuration, achieving a mean cross-validation accuracy of 83.15%. Evaluation on the independent test set yielded a final classification accuracy of 83.05%, with per-class F1-scores ranging from 0.73 to 0.89 (stop/up/yes). While lower than 3D CNN approaches (89.16%), the optimized SVM offers superior computational efficiency and provides a computationally efficient alternative compared to deep learning approaches with rigorous hyperparameter tuning as a practical baseline for lightweight speech command recognition.
This paper proposes an improved approach to deepfake classification by integrating a multi-level feature representation with a systematic, classifier-specific feature space optimization. The paper represents audio signals through three-level model: tabular spectral features, spectrogram-based images, and temporal seque...
Oleksandr K. Andronati, S. Antoshchuk, Anatolii Nikolenko et al.· Automation, Control, and Inf...· 0 citations
Under resource limits, NB is the preferred choice even though predictive accuracy does not differ significantly from SVM, and the practical distinction is computational: NB trained 22.5× faster, predicted 12× faster, used 2.9× less memory, and produced a 12× smaller model file.
P. Shanthini, S. Parthasarathy· New Mathematics and Natural...· 0 citations
Speaker identification is an important task in speech processing, with applications in security, authentication, forensics, and personalised human–computer interaction. This study presents a lightweight convolutional neural network (CNN)-based system for close-set identification of 40 speakers using Mel-Frequency Cepst...
N. K. Kaphungkui· JOURNAL OF MECHANICS OF CONT...· 0 citations
The results show that a controlled lightweight fusion framework can improve robustness while making the performance gap between speaker-overlapping and speaker-independent evaluation explicit, and improve robustness while making the performance gap between speaker-overlapping and speaker-independent evaluation explicit...
This paper evaluates a compact, from-scratch convolutional neural network (CNN) on the MNIST handwritten-digit benchmark along four simultaneous axes: classification accuracy against classical baselines, explainability faithfulness, adversarial and random-noise robustness, and feature-space separability. The CNN is imp...
I. Sengol· Natural Resources for Human...· 0 citations
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